SoroushVahidi/scidocs huggingface dataset:
A Critical Review of Recurrent Neural Networks for Sequence Learning
(2015)

Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as translating natural language, engaging in dialogue, and controlling a robot, often demand both capabilities. Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes. Unlike standard feedforward neural networks, recurrent networks retain a state that can represent information from an arbitrarily long context window. Although recurrent neural networks have traditionally been difficult to train, and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful large-scale learning with them. In recent years, systems based on long short-term memory (LSTM) and bidirectional (BRNN) architectures have demonstrated ground-breaking performance on tasks as varied as image captioning, language translation, and handwriting recognition. In this survey, we review and synthesize the research that over the past three decades first yielded and then made practical these powerful learning models. When appropriate, we reconcile conflicting notation and nomenclature. Our goal is to provide a selfcontained explication of the state of the art together with a historical perspective and references to primary research.
Open Graphs and Monoidal Theories
(2013)

String diagrams are a powerful tool for reasoning about physical processes, logic circuits, tensor networks, and many other compositional structures. The distinguishing feature of these diagrams is that edges need not be connected to vertices at both ends, and these unconnected ends can be interpreted as the inputs and outputs of a diagram. In this paper, we give a concrete construction for string diagrams using a special kind of typed graph called an open-graph. While the category of open-graphs is not itself adhesive, we introduce the notion of a selective adhesive functor, and show that such a functor embeds the category of open-graphs into the ambient adhesive category of typed graphs. Using this functor, the category of open-graphs inherits “enough adhesivity” from the category of typed graphs to perform double-pushout (DPO) graph rewriting. A salient feature of our theory is that it ensures rewrite systems are “type-safe” in the sense that rewriting respects the inputs and outputs. This formalism lets us safely encode the interesting structure of a computational model, such as evaluation dynamics, with succinct, explicit rewrite rules, while the graphical representation absorbs many of the tedious details. Although topological formalisms exist for string diagrams, our construction is discrete, finitary, and enjoys decidable algorithms for composition and rewriting. We also show how open-graphs can be parameterised by graphical signatures, similar to the monoidal signatures of Joyal and Street, which define types for vertices in the diagrammatic language and constraints on how they can be connected. Using typed open-graphs, we can construct free symmetric monoidal categories, PROPs, and more general monoidal theories. Thus open-graphs give us a tool for mechanised reasoning in monoidal categories.
Functorial Boxes in String Diagrams
(2006)

String diagrams were introduced by Roger Penrose as a handy notation to manipulate morphisms in a monoidal category. In principle, this graphical notation should encompass the various pictorial systems introduced in proof-theory (like Jean-Yves Girard’s proof-nets) and in concurrency theory (like Robin Milner’s bigraphs). This is not the case however, at least because string diagrams do not accomodate boxes — a key ingredient in these pictorial systems. In this short tutorial, based on our accidental rediscovery of an idea by Robin Cockett and Robert Seely, we explain how string diagrams may be extended with a notion of functorial box to depict a functor separating an inside world (its source category) from an outside world (its target category). We expose two elementary applications of the notation: first, we characterize graphically when a faithful balanced monoidal functor F : C −→ D transports a trace operator from the category D to the category C, and we then exploit this to construct well-behaved fixpoint operators in cartesian closed categories generated by models of linear logic; second, we explain how the categorical semantics of linear logic induces that the exponential box of proof-nets decomposes as two enshrined functorial boxes. ∗Invited paper at the conference Computer Science Logic 2006 in Szeged, Hungary. To appear in the proceedings of the conference. c © Springer Verlag. This research was partially supported by the ANR Project INVAL “Invariants algébriques des systèmes informatiques”.
A survey of graphical languages for monoidal categories
(2009)

This article is intended as a reference guide to various notions of monoidal categories and their associated string diagrams. It is hoped that this will be useful not just to mathematicians, but also to physicists, computer scientists, and others who use diagrammatic reasoning. We have opted for a somewhat informal treatment of topological notions, and have omitted most proofs. Nevertheless, the exposition is sufficiently detailed to make it clear what is presently known, and to serve as a starting place for more in-depth study. Where possible, we provide pointers to more rigorous treatments in the literature. Where we include results that have only been proved in special cases, we indicate this in the form of caveats.
Diagram Rewriting for Orthogonal Matrices: A Study of Critical Peaks
(2008)

One of the rules, which is similar to the Yang-Baxter equatio n, nvolves a map h : [0, π[ → [0, π[. To study the algebraic properties of h, we use the confluence of critical peaks in our rewrite system, and we introduce parametric diagramsdescribing the calculation of angles of rotations generated b y rewriting. In particular, h satisfies thetetrahedron equation(also called Zamolodchikov equation).
An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages
(2017)

This paper investigates neural characterbased morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarelyand unseen words gracefully. We evaluate on 14 languages and observe consistent gains over a state-of-the-art morphological tagger across all languages except for English and French, where we match the state-of-the-art. We compare two architectures for computing characterbased word vectors using recurrent (RNN) and convolutional (CNN) nets. We show that the CNN based approach performs slightly worse and less consistently than the RNN based approach. Small but systematic gains are observed when combining the two architectures by ensembling.
Understanding Citizen ’ s Continuance Intention to Use e-Government Website : a Composite View of Technology Acceptance Model and Computer Self-Efficacy
(2008)

This study aims to understand the fundamental factors influencing the citizen’s continuance intention to use eGovernment websites by using the Technology Acceptance Model (TAM) as a based theoretical model. Computer selfefficacy is adopted as an additional factor that influences the citizen’s continuance intention to use e-Government websites. To empirically test the proposed research model, the web-based survey was employed. The participants consisted of 614 country-wide citizens with at least a bachelor’s degree and an experience with e-Government websites. Regression analysis was conducted to test the model. The results revealed that perceived usefulness and perceived ease of use of e-Government websites and citizen’s computer self-efficacy directly enhanced citizen’s continuance intention to use e-Government websites. In addition, perceived ease of use of e-Government websites indirectly enhanced citizen’s continuance intention through perceived usefulness.
Common Sense Knowledge Based Personality Recognition from Text
(2013)

Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level , emotive words and other lexical clues such as number of first person or sec ond person words carry major role to identify personality associated with the te xt. In this work, we propose a new architecture for the same task using common sen se knowledge with associated sentiment polarity and affective labels. To extract the common sense knowledge with sentiment polarity scores and affect iv labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined comm n sense knowledge based features with phycho-linguistic features and freque ncy based features and later the features were employed in supervised classifier . We designed five SMO based supervised classifiers for five personality t raits. We observe that the use of common sense knowledge with affective and sentim ent information enhances the accuracy of the existing frameworks which use o nly psycho-linguistic features and frequency based analysis at lexical le ve .
Fascia lliaca block for analgesia after hip arthroplasty: a randomized double-blind, placebo-controlled trial.
(2013)

BACKGROUND AND OBJECTIVES
Fascia iliaca block (FIB) is often used to treat pain after total hip arthroplasty (THA), despite a lack of randomized trials to evaluate its efficacy for this indication. The objective of this study was to assess the analgesic benefit of FIB after THA. Our primary hypothesis was administration of FIB decreases the intensity of postoperative pain (numeric rating scale [NRS-11] score) compared with sham block (SB) in patients after THA.


METHODS
After institutional review board approval and informed consent, 32 eligible patients having THA were recruited. In the postoperative care unit, although all patients received intravenous morphine sulfate patient-controlled analgesia, patients reporting pain of 3 or greater on the NRS-11 scale were randomized to receive ultrasound-guided fascia iliaca (30 mL 0.5% ropivacaine) or SB (30 mL 0.9% NaCl) using identical technique, below fascia iliaca. The primary outcome was pain intensity (NRS-11) after FIB.


RESULTS
Thirty-two patients (16 in each group) completed the study; all patients received an FIB. There was no difference in pain intensity (NRS-11 = 5.0 ± 0.6 vs 4.7 ± 0.6, respectively) after FIB versus SB or in opioid consumption (8.97 ± 1.6 vs 5.7 ± 1.6 mg morphine, respectively) between the groups at 1 hour. The morphine consumption after 24 hours was similar in both groups (49.0 ± 29.9 vs 50.4 ± 34.5 mg, P = 0.88, respectively).


CONCLUSIONS
The evidence in these data suggests that the difference in average pain intensity after FIB versus SB was not significant (95% confidence interval, -2.2-1.4 NRS units).
A (sub)graph isomorphism algorithm for matching large graphs
(2004)

We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.
Image-based visual navigation for mobile robots
(2013)

We introduce a new image-based visual navigation algorithm that allows the Cartesian velocity of a robot to be defined with respect to a set of visually observed features corresponding to previously unseen and unmapped world points. The technique is well suited to mobile robot tasks such as moving along a road or flying over the ground. We describe the algorithm in general form and present detailed simulation results for an aerial robot scenario using a spherical camera and a wide angle perspective camera, and present experimental results for a mobile ground robot.
Knowledge sharing behavior of physicians in hospitals
(2003)

Sharing knowledge of Physicians within hospitals can realize potential gains enormously and is critical to be successful and survive in competitive environments. There is a need for empirical research to identify the factors that determine physician's behavior to share knowledge. This study investigates the factors that determine the physician's individual knowledge sharing behavior in his/her department. The purpose of this study is to examine empirically the physicians' knowledge sharing behavior The research models under investigation are the Theory of Reasoned Action (TRA) model and the Theory of Planned Behavior (TPBI model. These models are empirically examined and compared, using the survey results on physicians' knowledge sharing behavior collected from 286 physicians practicing in 28 departments in 13 tertiary hospitals in Korea. TPB model exhibited a good fit with the data and appeared to be superior to TRA in explaining physicians' intentions to share knowledge. Amended TPB model provided an important improvement In fit over that of original TPB model. In amended TPB model, subjective norms were found to have the strongest total effects on behavioral intentions to share knowledge of physicians through direct and indirect path by attitude. Attitude was found to be the second important factor influencing physicians' intentions. Perceived behavioral control was also found to have effect on the intentions to share knowledge though it was weaker than that of subjective norms or attitude. The implications for physician's knowledge sharing activities are discussed
vTPM: Virtualizing the Trusted Platform Module
(2006)

We present the design and implementation of a system that enables trusted computing for an unlimited number of virtual machines on a single hardware platform. To this end, we virtualized the Trusted Platform Module (TPM). As a result, the TPM’s secure storage and cryptographic functions are available to operating systems and applications running in virtual machines. Our new facility supports higher-level services for establishing trust in virtualized environments, for example remote attestation of software integrity. We implemented the full TPM specification in software and added functions to create and destroy virtual TPM instances. We integrated our software TPM into a hypervisor environment to make TPM functions available to virtual machines. Our virtual TPM supports suspend and resume operations, as well as migration of a virtual TPM instance with its respective virtual machine across platforms. We present four designs for certificate chains to link the virtual TPM to a hardware TPM, with security vs. efficiency trade-offs based on threat models. Finally, we demonstrate a working system by layering an existing integrity measurement application on top of our virtual TPM facility.
Action recognition from a distributed representation of pose and appearance
(2011)

We present a distributed representation of pose and appearance of people called the “poselet activation vector”. First we show that this representation can be used to estimate the pose of people defined by the 3D orientations of the head and torso in the challenging PASCAL VOC 2010 person detection dataset. Our method is robust to clutter, aspect and viewpoint variation and works even when body parts like faces and limbs are occluded or hard to localize. We combine this representation with other sources of information like interaction with objects and other people in the image and use it for action recognition. We report competitive results on the PASCAL VOC 2010 static image action classification challenge.
A Review of Sleep EEG Patterns . Part I : A Compilation of Amended Rules for Their Visual Recognition according to Rechtschaffen and Kales
(2006)

Task Force ‘Scoring of Polysomnographic Recordings’ of the German Sleep Society (DGSM): Andrea Rodenbeck, Ralf Binder, Peter Geisler, Heidi Danker-Hopfe, Reimer Lund, Friedhart Raschke, Hans-Günther Weeß, and Hartmut Schulz (Chairman) Department of Psychiatry, University of Göttingen, Göttingen, Germany Department of Psychiatry, Pfalzklinikum, Klingenmünster, Germany Department of Psychiatry, University of Regensburg, Regensburg, Germany Department of Psychiatry, Charité-CBF, Berlin, Germany Grüntenstr. 11, 80686 München (formerly Dept. of Pneumology, ASKLEPIOS Clinic Gauting, Germany) Institute of Rehabilitation Norderney, Norderney, Germany Department of Neurology, HELIOS-Klinikum Erfurt, and Free University of Berlin, Biopsychology, Germany
Surveys on attitudes towards legalisation of euthanasia: importance of question phrasing.
(2004)

AIM
To explore whether the phrasing of the questions and the response alternatives would influence the answers to questions about legalisation of euthanasia.


METHODS
Results were compared from two different surveys in populations with similar characteristics. The alternatives "positive", "negative", and "don't know" (first questionnaire) were replaced with an explanatory text, "no legal sanction", four types of legal sanctions, and no possibility to answer "don't know" (second questionnaire). Four undergraduate student groups (engineering, law, medicine, and nursing) answered.


RESULTS
In the first questionnaire (n = 684) 43% accepted euthanasia (range 28-50%), 14% (8-33%) did not, and 43% (39-59%) answered "don't know". Two per cent of the respondents declined to answer. In comparison with previous surveys on attitudes to euthanasia the proportion of "don't know" was large. The results of the second questionnaire (n = 639), showed that 38% favoured "no legal prosecution" (26-50%). However, 62% (50-74%) opted for different kinds of legal sanctions, and two of four groups expressed significantly different views in the two surveys. A proportion of 10% declined to answer the second questionnaire.


CONCLUSION
An introduction of an explanatory text and a wider range of response alternatives produced differences between the results of the two surveys conducted.
LOGO MATCHING ALGORITHM FEATURE‐BASED MATCHING RESULTS IMRESTAURANT(): MATLAB FOR FEATURE‐BASED RESTAURANT LOGO RECOGNITION
(2010)

This paper discusses the implementation of imrestaurant(), a MATLAB function for feature-based restaurant logo recognition. The algorithm works by first applying SIFT to a logo database and then creating a hierarchical vocabulary tree using K-means clustering. The input to the algorithm is a photograph of an image logo, which is first filtered to reduce noise. Then SIFT is applied to the input image, and the output descriptors are pushed to the vocabulary tree. Each image in the logo database is scored using TF-IDF weights and the ten highest scoring database logos are pairwise matched using the ratio test and RANSAC. The restaurant database image with the largest consensus set is searched for on yelp.com. This algorithm was shown to be robust against input image size, background noise, and shearing. Finally, future work in increasing the accuracy of feature detection as well as optical character recognition is discussed.
Ensemble of jointly trained deep neural network-based acoustic models for reverberant speech recognition
(2019)

Distant speech recognition is a challenge, particularly due to the corruption of speech signals by reverberation caused by large distances between the speaker and microphone. In order to cope with a wide range of reverberations in real-world situations, we present novel approaches for acoustic modeling including an ensemble of deep neural networks (DNNs) and an ensemble of jointly trained DNNs. First, multiple DNNs are established, each of which corresponds to a different reverberation time 60 (RT60) in a setup step. Also, each model in the ensemble of DNN acoustic models is further jointly trained, including both feature mapping and acoustic modeling, where the feature mapping is designed for the dereverberation as a front-end. In a testing phase, the two most likely DNNs are chosen from the DNN ensemble using maximum a posteriori (MAP) probabilities, computed in an online fashion by using maximum likelihood (ML)-based blind RT60 estimation and then the posterior probability outputs from two DNNs are combined using the ML-based weights as a simple average. Extensive experiments demonstrate that the proposed approach leads to substantial improvements in speech recognition accuracy over the conventional DNN baseline systems under diverse reverberant conditions.
We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods
(2016)

Recently, Wi-Fi has caught tremendous attention for its ubiquity. Motivated by Wi-Fi’s low cost and privacy preservation, researchers have been putting lots of investigation into its potential on action recognition and even person identification. In this paper, for bringing a new modality for multimedia community, we offer an comprehensive overview on these two topics in Wi-Fi. Also, through looking at these two topics from an unprecedented perspective, we could achieve generality instead of designing specific ad-hoc features for each scenario. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on computer vision methods. To minimize the effect of location dependency embedded in CSI, we propose a novel de-noising method based on Singular Value Decomposition (SVD) to eliminate the background energy and effectively extract the channel information of signals reflected by human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed methods. Also, we conclude the factors that would affect the performance and highlight a few potential issues that require further deliberations.
Face Photo-Sketch Synthesis and Recognition
(2012)

Today in Modern Society Face Recognition has gained much attention in the field of network multimedia access. After the 9/11 tragedy in India, the need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric recognition techniques is face recognition since face is the convenient way used by the people to identify each other. In this paper we are going to study a method for representing face which is based on the features which uses geometric relationship among the facial features like mouth, nose and eyes .Feature based face representation is done by independently matching templates of three facial regions i.e eyes, mouth and nose .Principal Component Analysis method which is also called Eigen faces is appearance based technique used widely for the dimensionality reduction and recorded a greater performance in face recognition. Here we are going to study about PCA followed by Feed Forward Neural Network called PCA-NN.
X-band 1kW SSPA using 20-way Hybrid Radial combiner for accelerator
(2016)

The X-band pulsed 1kW SSPA for XFEL has been designed and manufactured. Amplifier modules were combined by a 20-way Hybrid Radial combiner in order to reduce output loss. The manufactured 20-Way Hybrid Radial Combiner is capable of high output with a combining efficiency of 95%. The entire system is driven by Pulse and results in a Power Gain of 60dB with an Output Power of 1kW.